17,748 research outputs found
Learning to Recommend with Multiple Cascading Behaviors
Most existing recommender systems leverage user behavior data of one type
only, such as the purchase behavior in E-commerce that is directly related to
the business KPI (Key Performance Indicator) of conversion rate. Besides the
key behavioral data, we argue that other forms of user behaviors also provide
valuable signal, such as views, clicks, adding a product to shop carts and so
on. They should be taken into account properly to provide quality
recommendation for users. In this work, we contribute a new solution named NMTR
(short for Neural Multi-Task Recommendation) for learning recommender systems
from user multi-behavior data. We develop a neural network model to capture the
complicated and multi-type interactions between users and items. In particular,
our model accounts for the cascading relationship among different types of
behaviors (e.g., a user must click on a product before purchasing it). To fully
exploit the signal in the data of multiple types of behaviors, we perform a
joint optimization based on the multi-task learning framework, where the
optimization on a behavior is treated as a task. Extensive experiments on two
real-world datasets demonstrate that NMTR significantly outperforms
state-of-the-art recommender systems that are designed to learn from both
single-behavior data and multi-behavior data. Further analysis shows that
modeling multiple behaviors is particularly useful for providing recommendation
for sparse users that have very few interactions.Comment: Published in IEEE Transactions on Knowledge and Data Engineering
(TKDE
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Joint Training Capsule Network for Cold Start Recommendation
This paper proposes a novel neural network, joint training capsule network
(JTCN), for the cold start recommendation task. We propose to mimic the
high-level user preference other than the raw interaction history based on the
side information for the fresh users. Specifically, an attentive capsule layer
is proposed to aggregate high-level user preference from the low-level
interaction history via a dynamic routing-by-agreement mechanism. Moreover,
JTCN jointly trains the loss for mimicking the user preference and the softmax
loss for the recommendation together in an end-to-end manner. Experiments on
two publicly available datasets demonstrate the effectiveness of the proposed
model. JTCN improves other state-of-the-art methods at least 7.07% for
CiteULike and 16.85% for Amazon in terms of Recall@100 in cold start
recommendation.Comment: Accepted by SIGIR'2
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Yum-me: A Personalized Nutrient-based Meal Recommender System
Nutrient-based meal recommendations have the potential to help individuals
prevent or manage conditions such as diabetes and obesity. However, learning
people's food preferences and making recommendations that simultaneously appeal
to their palate and satisfy nutritional expectations are challenging. Existing
approaches either only learn high-level preferences or require a prolonged
learning period. We propose Yum-me, a personalized nutrient-based meal
recommender system designed to meet individuals' nutritional expectations,
dietary restrictions, and fine-grained food preferences. Yum-me enables a
simple and accurate food preference profiling procedure via a visual quiz-based
user interface, and projects the learned profile into the domain of
nutritionally appropriate food options to find ones that will appeal to the
user. We present the design and implementation of Yum-me, and further describe
and evaluate two innovative contributions. The first contriution is an open
source state-of-the-art food image analysis model, named FoodDist. We
demonstrate FoodDist's superior performance through careful benchmarking and
discuss its applicability across a wide array of dietary applications. The
second contribution is a novel online learning framework that learns food
preference from item-wise and pairwise image comparisons. We evaluate the
framework in a field study of 227 anonymous users and demonstrate that it
outperforms other baselines by a significant margin. We further conducted an
end-to-end validation of the feasibility and effectiveness of Yum-me through a
60-person user study, in which Yum-me improves the recommendation acceptance
rate by 42.63%
Deep Interest Network for Click-Through Rate Prediction
Click-through rate prediction is an essential task in industrial
applications, such as online advertising. Recently deep learning based models
have been proposed, which follow a similar Embedding\&MLP paradigm. In these
methods large scale sparse input features are first mapped into low dimensional
embedding vectors, and then transformed into fixed-length vectors in a
group-wise manner, finally concatenated together to fed into a multilayer
perceptron (MLP) to learn the nonlinear relations among features. In this way,
user features are compressed into a fixed-length representation vector, in
regardless of what candidate ads are. The use of fixed-length vector will be a
bottleneck, which brings difficulty for Embedding\&MLP methods to capture
user's diverse interests effectively from rich historical behaviors. In this
paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
challenge by designing a local activation unit to adaptively learn the
representation of user interests from historical behaviors with respect to a
certain ad. This representation vector varies over different ads, improving the
expressive ability of model greatly. Besides, we develop two techniques:
mini-batch aware regularization and data adaptive activation function which can
help training industrial deep networks with hundreds of millions of parameters.
Experiments on two public datasets as well as an Alibaba real production
dataset with over 2 billion samples demonstrate the effectiveness of proposed
approaches, which achieve superior performance compared with state-of-the-art
methods. DIN now has been successfully deployed in the online display
advertising system in Alibaba, serving the main traffic.Comment: Accepted by KDD 201
Try This Instead: Personalized and Interpretable Substitute Recommendation
As a fundamental yet significant process in personalized recommendation,
candidate generation and suggestion effectively help users spot the most
suitable items for them. Consequently, identifying substitutable items that are
interchangeable opens up new opportunities to refine the quality of generated
candidates. When a user is browsing a specific type of product (e.g., a laptop)
to buy, the accurate recommendation of substitutes (e.g., better equipped
laptops) can offer the user more suitable options to choose from, thus
substantially increasing the chance of a successful purchase. However, existing
methods merely treat this problem as mining pairwise item relationships without
the consideration of users' personal preferences. Moreover, the substitutable
relationships are implicitly identified through the learned latent
representations of items, leading to uninterpretable recommendation results. In
this paper, we propose attribute-aware collaborative filtering (A2CF) to
perform substitute recommendation by addressing issues from both
personalization and interpretability perspectives. Instead of directly
modelling user-item interactions, we extract explicit and polarized item
attributes from user reviews with sentiment analysis, whereafter the
representations of attributes, users, and items are simultaneously learned.
Then, by treating attributes as the bridge between users and items, we can
thoroughly model the user-item preferences (i.e., personalization) and
item-item relationships (i.e., substitution) for recommendation. In addition,
A2CF is capable of generating intuitive interpretations by analyzing which
attributes a user currently cares the most and comparing the recommended
substitutes with her/his currently browsed items at an attribute level. The
recommendation effectiveness and interpretation quality of A2CF are
demonstrated via extensive experiments on three real datasets.Comment: To appear in SIGIR'2
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
TribeFlow: Mining & Predicting User Trajectories
Which song will Smith listen to next? Which restaurant will Alice go to
tomorrow? Which product will John click next? These applications have in common
the prediction of user trajectories that are in a constant state of flux over a
hidden network (e.g. website links, geographic location). What users are doing
now may be unrelated to what they will be doing in an hour from now. Mindful of
these challenges we propose TribeFlow, a method designed to cope with the
complex challenges of learning personalized predictive models of
non-stationary, transient, and time-heterogeneous user trajectories. TribeFlow
is a general method that can perform next product recommendation, next song
recommendation, next location prediction, and general arbitrary-length user
trajectory prediction without domain-specific knowledge. TribeFlow is more
accurate and up to 413x faster than top competitors.Comment: To Appear at WWW 201
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
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